library(clusterProfiler)
##
## clusterProfiler v4.16.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
##
## Please cite:
##
## G Yu. Thirteen years of clusterProfiler. The Innovation. 2024,
## 5(6):100722
##
## Attaching package: 'clusterProfiler'
## The following object is masked from 'package:stats':
##
## filter
library(igraph)
##
## Attaching package: 'igraph'
## The following object is masked from 'package:clusterProfiler':
##
## simplify
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
seed.protein <- c("APP", "PSEN1", "PSEN2", "APOE", "TREM2", "CR1")
protein.info <- read.table("./9606.protein.info.v12.0.txt", sep='\t', header=T, check.names=F, comment.char="", quote="")
head(protein.info[,seq(2)])
## #string_protein_id preferred_name
## 1 9606.ENSP00000000233 ARF5
## 2 9606.ENSP00000000412 M6PR
## 3 9606.ENSP00000001008 FKBP4
## 4 9606.ENSP00000001146 CYP26B1
## 5 9606.ENSP00000002125 NDUFAF7
## 6 9606.ENSP00000002165 FUCA2
ppi <- read.table("./9606.protein.links.v12.0.txt", header = T, check.names = F, comment.char = "", quote = "")
seed.protein2 <- protein.info[protein.info$preferred_name %in% seed.protein, 1]
target.ppi <- ppi |> dplyr::filter(protein1 %in% seed.protein2 | protein2 %in% seed.protein2) |>
dplyr::filter(combined_score>600)
target.ppi$protein1 <- protein.info[match(target.ppi$protein1, protein.info$`#string_protein_id`), 2]
target.ppi$protein2 <- protein.info[match(target.ppi$protein2, protein.info$`#string_protein_id`), 2]
target.ppi |> write.table("./target_ppi.tsv", sep="\t", row.names=F, col.names=T, quote=F)
## load ppi network
target_ppi_g <- graph_from_data_frame(target.ppi, directed=FALSE)
target_ppi_g |> V() |> length()
## [1] 571
target_ppi_g |> E() |> length()
## [1] 1674
## simplify network
g_ppi <- simplify(target_ppi_g, remove.multiple = FALSE, remove.loops = TRUE)
g_ppi <- simplify(g_ppi, remove.multiple = TRUE, remove.loops = FALSE,
edge.attr.comb = "max")
## load TCMDATA locally
# remotes::install_github("Hinna0818/TCMDATA)
# library(TCMDATA)
devtools::load_all("/Users/hinna/TCMDATA")
## ℹ Loading TCMDATA
## compute node info
g_ppi <- compute_nodeinfo(g_ppi)
## Calculating degree / strength ...
## Calculating betweenness (unweighted) ...
## Calculating closeness (unweighted) ...
## Calculating eigenvector centrality ...
## Calculating PageRank ...
## Calculating coreness (k-core) ...
## Calculating local clustering coefficient ...
## Calculating eccentricity ...
## Detecting articulation points ...
## Calculating MCC ...
## Calculating MNC ...
## Calculating DMNC ...
## Calculating BottleNeck (BN) ...
## Calculating radiality ...
## Calculating Stress ...
## Calculating EPC ...
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## show basic metrics
V(g_ppi)$degree |> table()
##
## 1 2 3 4 5 45 54 74 100 244 330
## 374 145 27 13 6 1 1 1 1 1 1
## show the relationship between cci and degree and betweenness
library(tidygraph)
##
## Attaching package: 'tidygraph'
##
## The following object is masked from 'package:igraph':
##
## groups
##
## The following object is masked from 'package:stats':
##
## filter
library(tibble)
##
## Attaching package: 'tibble'
##
## The following object is masked from 'package:igraph':
##
## as_data_frame
library(ggfun)
## ggfun v0.2.0 Learn more at https://yulab-smu.top/
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.2
library(ggrepel)
library(ggiraph)
g_ppi |> as_tbl_graph() |>
activate('nodes') |>
tibble::as_tibble() |>
ggplot(aes(x = degree, y = clustering_coef, size = betweenness)) +
geom_point() +
geom_text_repel(data = td_filter(degree > 80), mapping = aes(label = name)) ## It is a zero-scale network
library(ggtangle)
## ggtangle v0.0.7 Learn more at https://yulab-smu.top/
library(ggiraph)
library(ggraph)
p1 <- ggtangle::ggplot(g_ppi, layout = "kk") +
geom_edge(alpha = 0.4) +
geom_text_repel(data = td_filter(degree > 60), aes(label = name), size = 3, segment.color = NA, colour = "red2", fontface = "bold") +
geom_point(aes(color = degree, size = betweenness)) +
scale_color_viridis_c()
p1
p11 <- ggraph(g_ppi, layout = 'kk') +
geom_edge_link() +
geom_point_interactive(
mapping = aes(
x = x, y = y,
color = betweenness,
tooltip = name,
data_id = name
)
) +
geom_text_repel(data = td_filter(degree > 60),
mapping = aes(x = x, y = y, label = name), bg.color ='white'
) +
scale_color_viridis_c() +
theme_graph()
girafe(ggobj = p11)
## louvain clustering
set.seed(42)
g_ppi <- run_louvain(g_ppi)
## No weights found. Running unweighted clustering.
## Running Louvain (Resolution: 1.0)...
## Done! Identified 5 modules (Modularity: 0.472).
p2 <- ggtangle::ggplot(g_ppi, layout = "kk") +
geom_edge(alpha = 0.4) +
geom_point(aes(color = as.factor(louvain_cluster), size = betweenness)) +
geom_text_repel(data = td_filter(degree > 60), aes(label = name), size = 3, segment.color = NA, colour = "red2", fontface = "bold") +
scale_color_viridis_d() +
labs(color = "Cluster")
p2
topnodes_info <- rank_ppi_nodes(g = g_ppi)
## Available metrics in graph: name, degree, strength, betweenness, betweenness_w, closeness, closeness_w, eigen_centrality, pagerank, coreness, clustering_coef, eccentricity, is_articulation, MCC, MNC, DMNC, BN, radiality, Stress, EPC, louvain_cluster
## Using the following metrics for ranking: degree, betweenness_w, closeness_w, eccentricity, radiality, Stress, MCC, MNC, DMNC, BN, EPC
g_ppi <- topnodes_info[["graph"]]
topnodes <- topnodes_info[["table"]]
## radar plot
node_profile <- get_node_profile(topnodes, node_name = "APOE",
metrics = c("degree",
"betweenness",
"closeness",
"MCC",
"MNC",
"pagerank",
"coreness",
"EPC"))
p_radar <- radar_plot(node_profile, title = "Centrality score of APOE")
p_radar
library(enrichplot)
## Warning: package 'enrichplot' was built under R version 4.5.1
## enrichplot v1.28.4 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
##
## Please cite:
##
## Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an
## R/Bioconductor package for Disease Ontology Semantic and Enrichment
## analysis. Bioinformatics. 2015, 31(4):608-609
##
## Attaching package: 'enrichplot'
## The following object is masked from 'package:ggfun':
##
## volplot
library(org.Hs.eg.db)
## Loading required package: AnnotationDbi
## Loading required package: stats4
## Loading required package: BiocGenerics
## Loading required package: generics
##
## Attaching package: 'generics'
## The following object is masked from 'package:TCMDATA':
##
## components
## The following objects are masked from 'package:igraph':
##
## components, union
## The following objects are masked from 'package:base':
##
## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:igraph':
##
## normalize, path
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
## unsplit, which.max, which.min
## Loading required package: Biobase
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
##
## Attaching package: 'Biobase'
## The following object is masked from 'package:TCMDATA':
##
## content
## Loading required package: IRanges
## Loading required package: S4Vectors
##
## Attaching package: 'S4Vectors'
## The following objects are masked from 'package:tidygraph':
##
## active, rename
## The following object is masked from 'package:clusterProfiler':
##
## rename
## The following object is masked from 'package:utils':
##
## findMatches
## The following objects are masked from 'package:base':
##
## expand.grid, I, unname
##
## Attaching package: 'IRanges'
## The following object is masked from 'package:tidygraph':
##
## slice
## The following objects are masked from 'package:TCMDATA':
##
## desc, slice
## The following object is masked from 'package:clusterProfiler':
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## slice
##
## Attaching package: 'AnnotationDbi'
## The following object is masked from 'package:tidygraph':
##
## select
## The following object is masked from 'package:TCMDATA':
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## select
## The following object is masked from 'package:clusterProfiler':
##
## select
##
module.genes <- g_ppi |> as_tbl_graph() |> activate('nodes') |>tibble::as_tibble()
module.ora.res <- compareCluster(name ~ louvain_cluster, data = module.genes, fun = enrichGO, keyType = "SYMBOL", OrgDb=org.Hs.eg.db, ont = "BP")
dotplot(module.ora.res, label_format = 70)
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the enrichplot package.
## Please report the issue at
## <https://github.com/GuangchuangYu/enrichplot/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# select cluster 1 nodes
c1_nodes <- V(g_ppi)$name[V(g_ppi)$louvain_cluster == "1"]
subg_cluster1 <- induced_subgraph(g_ppi, vids = c1_nodes)
# cluster 1 enrichment analysis
c1 <- enrichGO(c1_nodes, OrgDb='org.Hs.eg.db', keyType="SYMBOL", ont = "all")
c1go <- gglollipop(c1, line.col = "orange", split = "ONTOLOGY",
line.type = "dashed", palette = "PiYG", top_n = 5, show_count = F) +
facet_grid(ONTOLOGY ~ ., scales = "free_y") +
theme(strip.text.y = element_text(angle = 0))
c1go